i. At What Price are the laptops actually selling ?

This Histogram shows the most frequent retail prices for all stores in 2018. In Black is the median

## [1] "Last Recorded Prices are 406  and 530  on the same Day with a mean of 468 "

Here is given the last recorded prices for 2018

ii. Does price change with time?

Those Plots show different aggregations levels, can be used depending on the analysis we want, thus the granularity need. End of the weekdays is generally having higher retail prices, such as the year starting with month period from May to December.

iii. Are prices consistent across retail outlets?

Each box plots belongs to a specific stores, we can see a common trend across all stores in 2018. We also see that 5 stores tend to have a lower retail price than others with the median closer to 465.

Looking at times series, we can see that not all stores have the same time trend, but most of them do.

iv. How does price change with configuration?

FALSE `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Using an smooth approximator, we can see two differents trends, first a rapid increase in price while being at low configurations, and then the slope tend to stay constant and low, ending with a increase with highest configurations.

i. Where are the stores and customers locatd?

Enjoy looking at each stores and customers in London UK ! You can find there exact location by clicking on them ! We can see a big cluster of 545 clients/stores in the center of City London.

transform_coordinates: Is a convinient function from Gissr (on Gihtub) that use the cran-project SpTransform as source code but can directly use coordinates in a dataframe and return it in a dataframe. The spTransform methods provide transformation between datum(s) and conversion between projections (also known as projection and/or re-projection), from one unambiguously specified coordinate reference system (CRS) to another, prior to version 1.5 using Proj4 projection arguments.

ii. Which stores are selling the most?

The following histograms show two ways of analyzing the stores sales results: by the number of transactions or the sales revenues they each generated during 2018. The Store SW1P 3AU sold the most and with the highest revenues.

iii. How far would customers travel to buy a laptop ?

With this plot we can see the distance between Customers and Stores in terms of latitude and longitude.

iv. How far would customers travel to buy a laptop ? - Alternative

DistHarversine: The shortest distance between two points (i.e., the ’great-circle-distance’ or ’as the crow flies’), according to the ’haversine method’. This method assumes a spherical earth, ignoring ellipsoidal effects. The Haversine (’half-versed-sine’) formula was published by R.W. Sinnott in 1984, although it has been known for much longer. At that time computational precision was lower than today (15 digits precision). With current precision, the spherical law of cosines formula appears to give equally good results down to very small distances.

Each Unique Customer can be found here, swipe on the right and see the distance they need to travel to get to their store.

Histogram of the Distance between Clients and Stores, with median Distance being approximately 4203 meters.

i. How do the sales volume in each store relate to Acell’s revenues?

You can see the proportional revenues participation of each stores in 2018. SW1P 3AU still is the store contributing the most to Acell’s Revenues.

ii. How does this relationship depend on the configuration?

We can see that S1P 3AU propose higher configurations, while having the smallest % revenues participation out of the total revenues of the company, this could be because it sells higher priced configurations, thus selling less to customer during the year, only to a smaller client pool that wants a better PC for more productive computing work.

i. What are the details of each configuration? How does this relate to price?

Depending on the details of each configurations, we can see that some specs tend to increase the price higher, such as the screen size, high RAM and high battery life.

ii. Do all stores sell all configurations?

With this multiple facets barplots, you can spot which configuration is less or not sold depending on the store. S1P 3AU is not selling every configurations.

i. Which variables have the largest variability?

Based on the Histogram Grid and the Standard Errors Summary, Sodium, Potass and Vitamins have the largest variability.

ii. Which variables seem skedew?

Potassium, Fiber and Fat seem skewed. Cups could also be.

iii. Are there any values that seem extreme?

We can see that Fiber has at least 3 extremes values (2 classes away from the main cluster) have extremes values. We could check with some boxplots to better see what are those outliers.

i. Which pair of variables is most strongly correlated?

FALSE             calories     protein           fat        sodium       sugars
FALSE calories  1.00000000  0.03399166  0.5073732397  0.2962474981  0.569120535
FALSE protein   0.03399166  1.00000000  0.2023533963  0.0115588913 -0.286583967
FALSE fat       0.50737324  0.20235340  1.0000000000  0.0008219036  0.287152487
FALSE sodium    0.29624750  0.01155889  0.0008219036  1.0000000000  0.037058961
FALSE sugars    0.56912054 -0.28658397  0.2871524866  0.0370589612  1.000000000
FALSE potass   -0.07136125  0.57874284  0.1996367171 -0.0394380876  0.001413982
FALSE weight    0.69645215  0.23067141  0.2217141647  0.3125335701  0.460547135
FALSE cups      0.08919615 -0.24209861 -0.1575787041  0.1195841083 -0.032436100
FALSE vitamins  0.25984556  0.05479952 -0.0305139099  0.3315759640  0.072954382
FALSE fiber    -0.29521183  0.51400610  0.0140358654 -0.0707349230 -0.150948502
FALSE carbo     0.27060605 -0.03674326 -0.2849336855  0.3284091857 -0.452069189
FALSE                potass     weight        cups    vitamins       fiber
FALSE calories -0.071361247  0.6964521  0.08919615  0.25984556 -0.29521183
FALSE protein   0.578742837  0.2306714 -0.24209861  0.05479952  0.51400610
FALSE fat       0.199636717  0.2217142 -0.15757870 -0.03051391  0.01403587
FALSE sodium   -0.039438088  0.3125336  0.11958411  0.33157596 -0.07073492
FALSE sugars    0.001413982  0.4605471 -0.03243610  0.07295438 -0.15094850
FALSE potass    1.000000000  0.4205615 -0.50168832 -0.00263583  0.91150392
FALSE weight    0.420561534  1.0000000 -0.20171465  0.32043480  0.24629218
FALSE cups     -0.501688318 -0.2017146  1.00000000  0.13362965 -0.51369716
FALSE vitamins -0.002635830  0.3204348  0.13362965  1.00000000 -0.03871734
FALSE fiber     0.911503921  0.2462922 -0.51369716 -0.03871734  1.00000000
FALSE carbo    -0.365002934  0.1448053  0.35828371  0.25357897 -0.37908370
FALSE                carbo
FALSE calories  0.27060605
FALSE protein  -0.03674326
FALSE fat      -0.28493369
FALSE sodium    0.32840919
FALSE sugars   -0.45206919
FALSE potass   -0.36500293
FALSE weight    0.14480528
FALSE cups      0.35828371
FALSE vitamins  0.25357897
FALSE fiber    -0.37908370
FALSE carbo     1.00000000

Fiber and Potass seems to have a strong correlation

ii. How can we reduce the number of variables based on these correlations?

We could select the highest correlated variable (because of threat of multicollinearity) and removed them. In the context of a Regression, using VIF on our model would suggest us which explanatory variabes we should remove based on those correlations table.

iii. How would the correlations change if we normalized the data first?

FALSE             calories     protein           fat        sodium       sugars
FALSE calories  1.00000000  0.03399166  0.5073732397  0.2962474981  0.569120535
FALSE protein   0.03399166  1.00000000  0.2023533963  0.0115588913 -0.286583967
FALSE fat       0.50737324  0.20235340  1.0000000000  0.0008219036  0.287152487
FALSE sodium    0.29624750  0.01155889  0.0008219036  1.0000000000  0.037058961
FALSE sugars    0.56912054 -0.28658397  0.2871524866  0.0370589612  1.000000000
FALSE potass   -0.07136125  0.57874284  0.1996367171 -0.0394380876  0.001413982
FALSE weight    0.69645215  0.23067141  0.2217141647  0.3125335701  0.460547135
FALSE cups      0.08919615 -0.24209861 -0.1575787041  0.1195841083 -0.032436100
FALSE vitamins  0.25984556  0.05479952 -0.0305139099  0.3315759640  0.072954382
FALSE fiber    -0.29521183  0.51400610  0.0140358654 -0.0707349230 -0.150948502
FALSE carbo     0.27060605 -0.03674326 -0.2849336855  0.3284091857 -0.452069189
FALSE                potass     weight        cups    vitamins       fiber
FALSE calories -0.071361247  0.6964521  0.08919615  0.25984556 -0.29521183
FALSE protein   0.578742837  0.2306714 -0.24209861  0.05479952  0.51400610
FALSE fat       0.199636717  0.2217142 -0.15757870 -0.03051391  0.01403587
FALSE sodium   -0.039438088  0.3125336  0.11958411  0.33157596 -0.07073492
FALSE sugars    0.001413982  0.4605471 -0.03243610  0.07295438 -0.15094850
FALSE potass    1.000000000  0.4205615 -0.50168832 -0.00263583  0.91150392
FALSE weight    0.420561534  1.0000000 -0.20171465  0.32043480  0.24629218
FALSE cups     -0.501688318 -0.2017146  1.00000000  0.13362965 -0.51369716
FALSE vitamins -0.002635830  0.3204348  0.13362965  1.00000000 -0.03871734
FALSE fiber     0.911503921  0.2462922 -0.51369716 -0.03871734  1.00000000
FALSE carbo    -0.365002934  0.1448053  0.35828371  0.25357897 -0.37908370
FALSE                carbo
FALSE calories  0.27060605
FALSE protein  -0.03674326
FALSE fat      -0.28493369
FALSE sodium    0.32840919
FALSE sugars   -0.45206919
FALSE potass   -0.36500293
FALSE weight    0.14480528
FALSE cups      0.35828371
FALSE vitamins  0.25357897
FALSE fiber    -0.37908370
FALSE carbo     1.00000000

Nothing changes when we normalized the data before correlation matrices and plots since normalization already occurs when computing correlations.